[whatmodel.sh](../../README.md) / QED-Nano # QED-Nano LM Provers · released 2026-02 · 4.02B params · apache-2.0 **score 56.4** ±2.2 · #254 of 732 · from 3 benchmark results across 1 source Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks QED-Nano among every model on that task or test, worst to best.}

task  vs the others         rank  counts  tests
math  ▁▁▃▅█▆●█▄▂▁▁   #191 of 431     98%      3

math · #191 of 431 · 98% of its score

Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty            counts  why
MathArena ArXivMath January 2026 …   37.0%  ▂··●▂▄█▆▆▄▄█ beats 1/23   hard · avg 23%           40%  at its level
MathArena ArXivMath February 2026…   14.1%  ●▁▁▁█▇▄▃▁▃·▃ beats 0/24   very hard · avg 9.4%     34%  typical
MathArena ArXivMath December 2025…   26.5%  ●·▃▃█▁·▄·▁▁▁ beats 0/18   hard · avg 19%           24%  at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
MathArena ArXivMath / BrokenArXiv / ArX…        3   98% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        2% ▇
weighted average                                   = 56.4

_{Licences: MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run QED-Nano (Q4_K_M, needs ~2.5 GB, from [huggingface.co/ijohn07](https://huggingface.co/ijohn07/QED-Nano-Q4_K_M-GGUF/tree/89ba3761fbf39d184255a8625969cd745c16d505)) ```sh llama cli -hf ijohn07/QED-Nano-Q4_K_M-GGUF:q4_k_m ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf ijohn07/QED-Nano-Q4_K_M-GGUF:q4_k_m ``` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run QED-Nano (Q4_K_M, needs ~2.5 GB, from [huggingface.co/ijohn07](https://huggingface.co/ijohn07/QED-Nano-Q4_K_M-GGUF/tree/89ba3761fbf39d184255a8625969cd745c16d505)) ```sh lms get https://huggingface.co/ijohn07/QED-Nano-Q4_K_M-GGUF@q4_k_m -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: 8bit (~4.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/QED-Nano-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run QED-Nano (Q4_K_M, needs ~2.5 GB, from [huggingface.co/ijohn07](https://huggingface.co/ijohn07/QED-Nano-Q4_K_M-GGUF/tree/89ba3761fbf39d184255a8625969cd745c16d505)) ```sh ollama run hf.co/ijohn07/QED-Nano-Q4_K_M-GGUF:q4_k_m ``` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run QED-Nano (8bit, needs ~4.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/QED-Nano-8bit/tree/980adf7e0d31d04205a5d593669bc1ce0606dc51)) ```sh mlx_lm.chat --model mlx-community/QED-Nano-8bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/QED-Nano-8bit ``` ### Other ways to run it 1. Run it as published (BF16, needs ~8.04 GB, NVIDIA only, from [huggingface.co/lm-provers](https://huggingface.co/lm-provers/QED-Nano/tree/1016ddef8dd40552b97216ce34e3fece2fffa2bb)) ```sh vllm serve lm-provers/QED-Nano ``` Model repository and documentation: [huggingface.co/lm-provers](https://huggingface.co/lm-provers/QED-Nano) [interactive version](https://whatmodel.sh/model/qed-nano) · [JSON](https://whatmodel.sh/api?format=json&all=1)